M.S. in Engineering
Electrical & Computer Engineering
Henry M. Rowan College of Engineering
Air Force Research Laboratory
Electrical engineering; Speech processing systems
Electrical and Computer Engineering
Signal-to-noise ratio is defined as the ratio of a given transmitted signal to the background noise of the transmission medium. Signal-to-noise ratio (SNR) is a common concept found in all forms of electrical communications. The easiest way to measure the signal-to-noise ratio is through intrusive means in which a corrupt signal is compared to its original signal. This technique is inefficient and impractical because it requires the original signal and can only be used to theoretically test the noise properties of a channel rather than estimate the SNR of a communicated signal. Characteristics of speech signals can be used to develop non-intrusive methods for estimating the SNR of a signal. These methods do not require knowledge of the original speech signal for analysis.
In this thesis a Vector Quantization (VQ) based pattern recognition system approach is applied to estimate the SNR of speech signals. Features for the VQ system are derived from the speech signals through linear predictive analysis. The system is trained and tested on a range of 0 to 30 dB SNR in which codebook size, codebook spacing, training sets, and decision methods are studied to determine the best system architecture for a robust SNR estimation system for speech signals. The optimal feature for estimating the SNR of any speech signal regardless of the spectrum of the background noise is determined through analysis of testing results. An ensemble of classifiers approach is used to perform both decision level and distance level fusion using various combination rules to determine the best feature combination and fusion technique for a robust SNR estimating system for speech signals.
Ondusko, Russell Paul III, "Use of linear predictive features and pattern recognition techniques to develop a vector quantization based blind SNR estimation system" (2008). Theses and Dissertations. 708.